A dually emissive MPA-CdTe QDs@N, S-GQD nanosensor for sensitive and selective detection of 4-nitrophenol using two turn-off signals.

4-Nitrophenol (4-NP) is an extremely poisonous and carcinogenic phenol that poses serious health issues to humans. Therefore, it becomes highly demanded and urgent to determine 4-NP in water samples. In this study, we developed a facile and effective dually-emissive nanosensor containing simply mixed CdTe quantum dots (CdTe QDs) and N, S modified graphene quantum dots (N, S-GQDs) for 4-NP. The synthesized CdTe QDs and N, S-GQDs exhibited excitation-independent emission located at 540 nm and 420 nm, respectively. The nanosensor displayed two turn-off fluorescent signals when exposed to 4-NP. The degree of quenching varied depending on the excitation wavelength range used, which can be explained by the quenching phenomenon based on the inner filter effect (IFE). Moreover, analysis of the recorded excitation-emission matrix (EEM) data using the parallel factor analysis (PARAFAC) technique revealed a negative emission spectrum corresponding to non-emissive 4-NP. On the other hand, the species with no peak in fluorescence data had a negative spectrum as the PARAFAC emission loading. Under the optimized conditions, the CdTe QDs@GQD nanosensor achieved fast and highly sensitive detection of 4-NP within the concentration range of 0.0-30.0 µM, with a detection limit of 0.52 µΜ.

Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods.

To address the growing concern of honey adulteration in Canada and globally, a quantitative NMR method was developed to analyze 424 honey samples collected across Canada as part of two surveys in 2018 and 2019 led by the Canadian Food Inspection Agency. Based on a robust and reproducible methodology, NMR data were recorded in triplicate on a 700 MHz NMR spectrometer equipped with a cryoprobe, and the data analysis led to the identification and quantification of 33 compounds characteristic of the chemical composition of honey. The high proportion of Canadian honey in the library provided a unique opportunity to apply multivariate statistical methods including PCA, PLS-DA, and SIMCA in order to differentiate Canadian samples from the rest of the world. Through satisfactory model validation, both PLS-DA as a discriminant modeling technique and SIMCA as a class modeling method proved to be reliable at differentiating Canadian honey from a diverse set of honeys with various countries of origins and floral types. The replacement method of optimization was successfully applied for variable selection, and trigonelline, proline, and ethanol at a lower extent were identified as potential chemical markers for the discrimination of Canadian and non-Canadian honeys.

A colorimetric assay and MCR-ALS analysis of the peroxidase-like activity of poly (N-phenylglycine) functionalized with polyethylene glycol (PNPG-PEG) nanozyme for the determination of dopamine.

This report describes, for the first time, the coupling of UV-visible spectroscopy with multivariate curve resolution-alternative least-squares (MCR-ALS) algorithm to study peroxidase-like catalytic reaction of polyethylene glycol-functionalized poly (N-phenyl glycine) (PNPG-PEG) as an efficient and intrinsic peroxidase mimic activity (PMA) class of conducting organic polymer for selective detection of dopamine (DA) in the PNPG-PEG + TMB + H2O2 reaction system. PNPG-PEG was produced by means of a chemical route using ammonium persulphate (APS) as an oxidant agent of N-phenyl glycine monomer. The chemical composition, morphology, and thermal behavior of PNPG-PEG were examined by various instrumental techniques. PNPG-PEG exhibited significant peroxidase-mimic activity to catalyze the oxidation 3,3',5,5'- tetramethylbenzidine (TMB) substrate to oxidized TMB (oxTMB). The qualitative and quantitative determination of the oxidized TMB can easily be detected by the naked-eye and the recorded UV-vis absorbance spectra at 652 nm, respectively. Owing to the superior peroxidase-mimic activity of PNPG-PEG, the colorimetric detection of dopamine was successfully achieved at pH 4.0. Under optimal conditions, acceptable linear dependency was recorded in the concentration range of 5.1-125.0 µM, with a limit of detection (LOD) and limit of quantification (LOQ) equal to 4.6 µM and 13.8 µM (S/N = 3), respectively. Furthermore, this colorimetric assay was successfully used for quantitative analysis of dopamine in fetal bovine serum (FBS) and horse serum (HS) samples with recoveries in the range of 97-105% and 100-122%, respectively. After resolving the bilinear data matrix using MCR-ALS, three chemical components were found for different concentrations and pure spectral profiles. Based on the resolved profiles, the presence of free, slightly penetrated, and majorly penetrated TMB molecules entering the polymeric structure can be easily detected using MCR-ALS as an available statical method without any complex separation instruments. This peroxidase mimetic nanozyme as a visual, simple, low-cost, sensitive, and reproducible colorimetric platform can provide great potential applications in the monitoring and diagnosis of dopamine-related diseases.

Unsupervised recognition of components from the interaction of BSA with Fe cluster in different conditions utilizing 2D fluorescence spectroscopy.

The excitation-emission fluorescence spectroscopy combined with three-way analysis was applied for discriminating the pure BSA and BSA/Fe3O(OAc)6ClO4 (Fe) using unsupervised classification methods. Herein, the interaction of bovine serum albumin (BSA) and Fe clusters as an artificial enzyme is studied by extracting the intrinsic excitation-emission (EEM) fluorescence of BSA. The conformation of BSA changes with pH, temperature, and Fe concentration. Three-way fluorescence data were recorded for BSA and BSA/Fe during different days. The obtained results showed that the Fe clusters cause changes in the structure of BSA conformation as a function of pH, temperature, and Fe concentration. Also, the denaturation pathway of the BSA molecule is significantly different in the presence of Fe clusters. Both techniques of PARAFAC and PCA were used in the excitation-emission fluorescence matrices (EEM) of solutions at three different pH (5.0, 7.0, and 9.0) and temperatures (15.0, 25.0, and 35.0 °C) values. Also, we reported the results of the change in concentrations of Fe (4.0, 6.0, and 8.0 mg) using these methods. These three amino acids (tyrosine, tryptophan, and phenylalanine) indicate all datasets and their similarities and differences. The spectral differences were more remarkable in different pH values compared to different temperatures. Also, we could distinguish between the groups of protein samples properly in different concentrations of Fe using low-cost EEM spectral images and PARAFAC.

Coupling of multivariate curve resolution-alternating least squares and mechanistic hard models to investigate antibody purification from human plasma using ion exchange chromatography.

A two steps proposal for the purification of immunoglobulin G from human blood plasma is investigated. The first step is precipitation using cold ethanol based on the Cohn method with some modification and the second step is a chromatographic separation by DEAE-Sepharose FF resin as a weak anion exchanger. The presence of interferent in the region3 of chromatographic fractions, which is co-eluted with IgG, restricts the application of the mechanistic chromatography model. Therefore, multivariate cure resolution-alternating least squares (MCR-ALS) as a soft method is employed on measured absorbance data matrix from eluted fractions to recover pure concentration and spectral profiles. Besides, possible solutions for resolved concentration and spectral profiles are investigated. The reaction-dispersive model as a mechanistic hard model for the column is utilized for the evaluation of the ion exchange chromatography. Using a genetic algorithm as a global optimization method, mobile phase modulator (MPM) adsorption model parameters such as ß, kdes,0, and Keq,0, were fitted to the concentration profiles from MCR-ALS as 1.96, 2.87×10-4 m3 mol-1s-1, and 1883, respectively. Furthermore, a new resampling incorporated non-parametric statistics is conducted to assess parameters' uncertainty. Values of 2.00, 1.10×10-3 m3 mol-1s-1, and 549.80 are estimated median, and values of 0.05, 2.5×10-3, and 691.00 are calculated interquartile range (IQR) for ß, kdes,0, and Keq,0, respectively. Finally, results show three and two outliers for ß and kdes,0, respectively.

Colorimetric assay for the detection of dopamine using bismuth ferrite oxide (Bi2Fe4O9) nanoparticles as an efficient peroxidase-mimic nanozyme.

This work describes the preparation of ternary bismuth ferrite oxide nanoparticles (Bi2Fe4O9 NPs) with an enzyme mimetic activity for dopamine (DA) qualitative and quantitative detection. Bi2Fe4O9 NPs were prepared using a facile, low cost, and one-pot hydrothermal treatment. The chemical composition, morphology, and optical properties of Bi2Fe4O9 nanozyme were characterized using different techniques such as Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction pattern (XRD), X-ray photoelectron spectroscopy (XPS), thermo-gravimetric analysis (TGA), dynamic light scattering (DLS), field-emission scanning electron microscopy (FESEM) imaging, FESEM-energy dispersive X-ray spectroscopy (EDS), UV-vis absorption, and fluorescence emission spectroscopy. Bi2Fe4O9 NPs were utilized to catalyze the oxidation of a typical chromogenic peroxidase substrate, 3,3',5,5'-tetramethylbenzidine (TMB), to form the blue-colored oxidized product (oxTMB), in the presence of hydrogen peroxide (H2O2). All reactions occurred in acetate buffer solution (pH 3.5) to generate hydroxyl radicals (•OH) and the kinetics were followed by UV-vis absorbance at 654 nm. The steady-state kinetic parameters were obtained from the Michaelis-Menten equation and exhibited a good catalytic efficiency of Bi2Fe4O9 NPs as enzyme mimetics. Michaelis-Menten constant (Km) values were estimated as 0.07 and 0.73 mM for TMB and H2O2, respectively. The presented method is efficient, rapid, cost-effective, and sensitive for the colorimetric detection of dopamine with a linear range (LR) from 0.15 to 50 µM and a detection limit (LOD) of 51 nM. The proposed colorimetric sensor was successfully applied for the detection of different concentrations of dopamine in spiked fetal bovine serum (FBS) and horse serum (HS) samples. It is anticipated that Bi2Fe4O9 nanozyme holds great potential in biomedical analysis and diagnostic applications of dopamine-related diseases.

Metabolomics approach to study in vivo toxicity of graphene oxide nanosheets.

Although graphene oxide (GO) nanosheets are widely used in different fields, the mechanism of their toxicity remains relatively unknown. NMR-based metabolomics was used to study in vivo time and dose-dependent toxicity of GO nanosheets in mice. Sixty serum samples from mice in four different time intervals including 24 and 72 h and 7 and 21 days after injection of 0-, 1-, and 10-mg/kg b.w. were analyzed based on 1 HNMR spectra of each sample and multivariate methods. In comparison with the control group, 12 changed metabolites were identified in GO nanosheet-treated mice groups. These metabolites are involved in steroid hormone biosynthesis and steroid biosynthesis pathways. It was seen that the time factor is more important than the dose factor and the groups were separated in a time direction, completely. We found that GO nanosheets has toxicity and can affect steroidal hormones. However, this study shows that after 21 days, the treated groups regardless of their GO nanosheet dose are very close to the control group. This means that in one step exposure to GO nanosheets, their toxicity diminished after 21 days.

Beyond principal components: a critical comparison of factor analysis methods for subspace modelling in chemistry.

Multivariate data analysis tools have become an integral part of modern analytical chemistry, and principal component analysis (PCA) is perhaps foremost among these. PCA is central in approaching many problems in data exploration, classification, calibration, modelling, and curve resolution. However, PCA is only one form of a broader group of factor analysis (FA) methods that are rarely employed by chemists. The dominance of PCA in chemistry is primarily a consequence of history and convenience, but this has obscured the potential advantages of other FA tools that are widely used in other fields. The purpose of this article, which is intended for those who are already familiar with the mathematical foundations and applications of PCA, is to develop a framework to relate PCA to other commonly used FA methods from the perspective of chemical applications. Specifically, PCA is compared to maximum likelihood factor analysis (MLFA), principal axis factorization (PAF) and maximum likelihood PCA (MLPCA). Similarities and differences are highlighted with regard to the assumptions and constraints of the models, algorithms employed, and calculation of scores and loadings. Practical aspects such as data dimensionality, preprocessing, rank estimation, improper solutions (Heywood cases), and software implementation are considered. The performance of the four methods is compared using both simulated and experimental data sets. While PCA provides the most reliable estimates when measurement error variance is uniform (homoscedastic noise) and MLPCA works best when the error covariance matrix is explicitly known, MLFA and PAF have the distinct advantage of providing information about measurement uncertainty and adapting to situations of unknown heteroscedastic errors, eliminating the need for scaling. Moreover, MLFA in particular is shown to be tolerant to deviations from model linearity. These results make a strong case for increased application of other FA methods in chemistry.

Molecularly imprinted polymer grafted on paper and flat sheet for selective sensing and diagnosis: a review.

Molecularly imprinted polymers are efficient and selective adsorbents which act as artificial receptors for desired compounds with the ability to recognize the size, shape, and functional groups of the compounds simultaneously. A molecularly imprinted polymer is prepared by the polymerization of functional monomers around a template (analyte) molecule. Afterward, the removal of the template from the polymer matrix leaves a selective cavity behind. The fabrication and development of molecularly imprinted polymers grew rapidly, due to their low cost, simple preparation, selectivity, sensitivity, and stable physicochemical properties. Traditionally, molecularly imprinted polymers can be synthesized using two main methods, namely bulk and surface imprinting. For more efficient use of the latter method, researchers have developed molecularly imprinted polymers grafted on the solid-phase matrix (substrate). This grafting technique would be particularly useful for surface imprinting of macromolecules, such as proteins. Cellulose fibers of papers with unique properties such as being abundant, retaining a porous structure, having good adsorption properties, and possessing hydroxyl groups naturally have gained much attention as substrate. The goal of this review is to introduce molecularly imprinted polymer-grafted or molecularly imprinted polymer-coated paper, as an interesting, simple, and efficient method in the detection and separation of small and large molecules. Therefore, in the present paper, several recent preparation techniques and applications of molecularly imprinted polymer-grafted paper are reviewed and discussed in detail. Green, cost-effective, selective, and sensitive paper-based sensor prepared via grafting molecularly imprinted polymer on paper surface with the potential use for online detection trace of analytes in the point-of-care testing.

Perception on aggregation induced multicolor emission and emission centers in carbon nanodots using successive dilution, anion exchange chromatography, and multi-way statistics.

Exploration in the way of understanding the optical behavior and structure of carbon nanodots has been increased due to their vast application. Their emission dependency on excitation wavelengths is the more prevalent and controversial subject. In this report we considered the optical structure of hydrothermally synthesized carbon nanodots using citric acid and 2,3-diaminopyridine as precursors. The presence of different emission centers experimented through anion exchange chromatography which resulted in fractions with more unique optical structures. The quantum confinement effect and energy exchange between different types of carbon nanodots, due to aggregation in higher concentration levels, was studied applying a stepwise dilution experiment. Analysis of the experimental data was done through the parallel factor analysis and the trajectory pattern recognition which resolved more about optical interactions and the presence of different emission centers in different particles. Results from infrared spectroscopy confirmed the dominating density of carboxyl functional groups on the nanodots with negative surface charges and higher influence of amine groups on dots with positive surface charges.

Antibodies purification from human plasma using fractionation, chromatography and gel electrophoresis assisted by multivariate analysis of complimentary absorption and fluorescence spectra.

Employing simple precipitation (fractionation) using Cohn method and weak anion exchange chromatography with DEAE resin, antibodies such as Immunoglobulin G are purified from human plasma. Fractions are eluted from column in four different regions depending on washing NaCl concentrations. Absorbance and excitation-emission fluorescence spectral data are measured for separated chromatographic fractions and analyzed using Multivariate Curve Resolution- Alternating Least Squares (MCR-ALS) and Parallel Factor Analysis (PARAFAC) techniques. Resolved concentration and spectral profiles provided information about existing components in each fraction. Protein and non-protein components are distinguished considering their resolved pure spectra and information from the two applied spectroscopic techniques is complementary. A number of components displayed both fluorescence and absorbance signals. When concentration of component (protein or non-protein) in sample is low and no significant absorbance signal is observed, sensitive fluorescence is useful to recognize the component and for non-fluorescent components absorbance spectra are utilized. Electrophoresis is utilized for separation of proteins in each fraction and showed that one distinguished protein from fluorescence and/or absorbance data can be a group of proteins with similar pure spectra and retention volume. Results showed presence of two protein in the first region (IgM and IgA), a group of proteins in second region (IgM, α-globulin, and IgG), a pure protein in third region (IgG), and a group of ß-globulin proteins in fifth region. It is well and clearly shown that multivariate analysis of different data sets with complementary information is necessary for better interpretation of such technically simple and biochemically complicated systems.

PARAFAC study of L-cys@CdTe QDs interaction to BSA, cytochrome c and trypsin: An approach through electrostatic and covalent bonds.

Utilizing fluorescence spectroscopy, non-covalent and covalent interactions of L-cys@CdTe quantum dots to bovine serum albumin (BSA), cytochrome c and trypsin were investigated. L-cys@CdTe QDs with the emission maximum at 530 nm and an average diameter of 2.6 nm were synthesized in the aqueous medium. Formaldehyde, N-(3-dimethylaminopropyl)-N-ethylcarbodiimide hydrochloride (EDC) with N-hydroxysuccinimide (NHS), and glutaraldehyde was applied as cross-linkers. In the case of both electrostatic and covalent strategies PARAFAC, as a powerful multi-way chemometrics technique, was utilized to analyze fluorescence excitation-emission (EEM) spectra. For non-covalent and covalent bonding, two and three significant components composed the PARAFAC models. Resolved EEM shows that in the presence of formaldehyde, a new component with an emission peak similar to BSA was obtained. Using EDC-NHS cross-linker, the fluorescence peak of the newly formed component was in a distinct wavelength with similar emission intensity, compared to L-cys@CdTe QDs and BSA. Employing glutaraldehyde, a distinguished component was easily detected at emission wavelengths higher than that of L-cys@CdTe QDs and proteins. It was concluded that the choice of cross-linker is a critical step to create different emission spectra when dealing with nano-bio-conjugations. This study shows that glutaraldehyde cross-linker leads to increase sensitivity, selectivity, and accuracy of protein analysis.

Systematic investigation of the measurement error structure in a smartphone-based spectrophotometer.

Smartphones are state-of-the-art devices with several interesting features which make them promising for analytical purposes. After modification to a spectrophotometer (smart spectrophotometer), they can be utilized for the quantitative or qualitative applications. Although smartphones have widely been applied for sensing∖biosensing purposes, the error structure/type of their outputs remained unexplored. Error structure information values the objects/channels in a given data set and variables have the same importance when the noise has identical independent distribution (i.i.d). Otherwise, error structure weights them for further data analysis. In this contribution, a smartphone-based spectrophotometer was constructed integrating simple optical elements-a tungsten lamp as source and a piece of digital versatile disc (DVD) as a reflecting diffraction grating to investigate the error sources of the smartphone-spectrophotometer. For this purpose, error covariance matrices (ECMs) were calculated using a series of replication capturing error information. Afterwards, PCA and MCR-ALS were employed for the decomposition of the ECMs and resolved profiles were translated to the error types. Finally, proportional error as a heteroscedastic noise was highlighted as the most important source of variation in the error structure of the smartphone-based spectrophotometer.

Multiway data analysis approach toward understanding of photoluminescence and energy transfer in carbon nanodots.

In this study, dilution analysis and anion exchange chromatography (AEC) were employed to provide insights into the photoluminescence (PL) of carbon nanodots (CNDs). A stepwise dilution process revealed that some of the fluorophores with higher energy emission were quenched in the high concentration solution and appeared in the dilute solutions. AEC fractionation led to seven sorts of CND fractions with similar surface charges. The fractionation for this CND mixture showed that excitation wavelength dependence was lower for separated CND particles. The wavelength dependence of excitation spectra could be due to energy exchange between particles that was reduced in diluted solutions and separated fractions. Multivariate analysis of AEC's data demonstrated that there were five distinct fluorophores, which formed the total CND emission. It is interesting that none of these fluorophores had a clear contribution to the surface charge of the CND particles. Further characterization through FTIR spectroscopy and 1 H NMR revealed that optical properties of CNDs did not follow the surface functional groups in CNDs. This situation means that the optical behaviour of particles and their fluorophores differed depending on the surface functional groups.

FTICR mass spectrometry-based multivariate analysis to explore distinctive metabolites and metabolic pathways: A comprehensive bioanalytical strategy toward time-course metabolic profiling of Thymus vulgaris plants responding to drought stress.

In this research, metabolic profiling/pathways of Thymus vulgaris (thyme) plant were assessed during a water deficit stress using an FTICR mass spectrometry-based metabolomics strategy incorporating multivariate data analysis and bioinformatics techniques. Herein, differences of MS signals in specific time courses after water deficit stress and control cases without any timing period were distinguished significantly by common pattern recognition techniques, i.e., PCA, HCA-Heatmap, and PLS-DA. Subsequently, the results were compared with supervised Kohonen neural network (SKN) ones as a non-linear data visualization and capable mapping tool. The classification models showed excellent performance to predict the level of drought stress. By assessing variances contribution on the PCA-loadings of the MS data, the discriminant variables related to the most critical metabolites were identified and then confirmed by ANOVA. Indeed, FTICR MS-based multivariate analysis strategy could explore distinctive metabolites and metabolic pathways/profiles, grouped into three metabolism categories including amino acids, carbohydrates (i.e., galactose, glucose, fructose, sucrose, and mannose), and other metabolites (rosmarinic acid and citrate), to indicate biological mechanisms in response to drought stress for thyme. It was achieved and approved through the MS signals, genomics databases, and transcriptomics factors to interpret and predict the plant metabolic behavior. Eventually, a comprehensive pathway analysis was used to provide a pathway enrichment analysis and explore topological pathway characteristics dealing with the remarkable metabolites to demonstrate that galactose metabolism is the most significant pathway in the biological system of thyme.

Unsupervised classification of PSII with and without water-oxidizing complex samples by PARAFAC resolution of excitation-emission fluorescence images.

The potential of excitation-emission fluorescence spectroscopy combined with three-way analysis was investigated for discriminating the photosystem II (PSII) (with the water-oxidizing complex) and without the water-oxidizing complex (wPSII) using unsupervised classification methods. The water-oxidizing complex within PSII carry out the reaction of water splitting which is as a vital process on the earth. Therefore, discriminating the presence of the water-oxidizing complex in protein samples is crucial. Low cost and accurate spectroscopic determination of the amount of clusters inside PSII or any other protein containing species are important when investigating the inclusion and exclusion of such clusters into and from species. Fluorescence data of samples were similar, and we showed the potential usefulness of multivariate methods, such as parallel factor analysis (PARAFAC) and principal component analysis (PCA) for recognition of the two types of samples. Both techniques were applied to the excitation-emission fluorescence matrices (EEM) of solutions at two of different pH values (2.0 and 12.0). Three fluorescent components were found for all samples that are related to tyrosine (Tyr), tryptophan (Trp) and phenylalanine (Phe) amino acids. These three amino acids are representative of all datasets and indicate their similarities and differences. We then found the effectual wavelengths for separation of samples in a specific acidity, including the excitation wavelengths of 220 and 230 nm and the emission wavelengths of 300 and 305 nm. The acidity of the solutions has various influences on the conformation of proteins. In PSII and PSII the without water-oxidizing complex samples conformational changes can change their spectra which was applied for discrimination purpose. This separation was better in pH = 12.0. We also showed the effect of time on small conformational changes within datasets were higher in pH = 2.0. In the end, for indicating the high distribution of spectral data from proteins which is the result of conformational changes, we compared the distribution of measured spectral data with that from a simple organic molecule, fluorescein. Altogether, we could distinguish between the two groups of protein samples properly at pH = 12.0 using low-cost EEM spectral images and PARAFAC.

Unveiling Adsorption of Boron Dipyrromethene Conjugated PbS Nanocrystals on Pt Electrode Surface: An Approach Using Electrogenerated Chemiluminescence Spooling Spectra and Multivariate Analysis.

This research focuses on the adsorption and molecular scale communication mechanism of PbS-BDY (BDY, boron dipyrromethene), a nanohybrid system of nanocrystal (NC) and a π-conjugated molecule, investigated through the electrogenerated chemiluminescence (ECL) spooling spectra and multivariate analysis. The results show that the charge transmitted from the excited state of BDY+ to the surface states of PbS NCs leads to emission quenching of BDY and emission enhancement of PbS NCs at 986 nm. Also, the essence tendency of unpassivated sulfur atoms on (100) facets of the PbS NCs acts as a force for adsorption of PbS NCs on the surface of Pt electrode. This phenomenon was proved by conjugation of BDY as an ECL active compound to the PbS NCs and multivariate analysis of augmented data at different scan rates. The obtained results from multivariate analysis reveal that adsorption of PbS-BDY and charge transfer from BDY to surface states of PbS NCs are independent of the scan rate.

Fluorescence Based Investigation of Temperature-Dependent Pb2+-Specific 8-17E DNAzyme Catalytic Sensor.

The 8-17E DNAzyme is a temperature-dependent DNA metalloenzyme catalyzing RNA trans esterification in the presence of Pb2+ metal ions. Labeling the stems of the substrate and DNAzyme with the Cy3 and Cy5 respectively, the considered DNAzyme was studied by the fluorescence spectroscopy. The temperature-dependent variability of the Pb2+-specific 8-17E DNAzyme catalytic sensor was investigated trough a number of successive temperature fluctuations from 4 to 25 °C to obtain information. Investigating underlined biochemical system reveals that in this sensor, free single strands Enzyme (Cy5-E) and Substrate (Cy3-S) have higher fluorescence intensities than hybridized forms, suggesting that the fluorophores are in a contact quenched. Increasing the temperature has three effects: 1) Fluorescence intensities for the free fluorophores were reduced, 2) stability of the hybridized form was reduced and cleavage of substrate in presence of Pb2+was occurred, and 3) conformation of ES hybridized form was changed (before cleavage). As a result of conformation changes in ES, S was more affected than E in the ES. Pb2+ ion shows quenching effect on both fluorophores and in the absence of N2(g) purge the effect was more considerable. A main goal that we had in mind was to find if significantly lower concentrations of Pb2+ and ES, compared to previous reports, can generate any observable cleavage in substrate. Analysis of the cleavage reaction for 50 nM ES indicates that S is cleaved at 25 °C in presence of N2(g) and 0.5 µM Pb2+, while in same condition no apparent change occurs in the 4 or 10 °C. The rapid, sensitive and low cost strategy presented here can be applicable to study temperature-dependent behavior of other nucleic acid-based biosensors.

Effect of drought stress on metabolite adjustments in drought tolerant and sensitive thyme.

Drought is one of the most important threats to plants and agriculture; therefore, understanding of the mechanism of drought tolerance is crucial for breeding of drought tolerant plants. Here, we assessed effects of four levels of drought (90%, 55%, 40% and 25% FC) on some physiological criteria and metabolite adjustment of two different drought-responsive thyme plants (Thymus vulgaris as drought sensitive and T. Kotschyanus as drought tolerant species), using 1H-NMR. Among three physiological parameters and 18 identified metabolites, species × treatment effects were significant (P ≤ 0.01) for leaf temperature, acetic acid, citric acid, fumaric acid, malic acid, succinic acid, fructose, sucrose and serine. RWC, chlorophyll and carotenoids content, glucose, alanine and choline were affected by simple effects of species and treatment. Correlation analysis revealed that there is a different correlation between physiological parameters and metabolites in both species. This analysis also revealed that, by ignoring the correlation between malic acid and succinic acid in T. vulgaris, there was no significant correlation between TCA intermediate in both species. According to results, sugars, amino acid and energy metabolism were affected by drought and, among them, TCA intermediates had more alternation in two studied species so, this cycle and its intermediates probably have more prominent role than other identified metabolites in the induction of drought tolerance.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection.

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. MATERIALS & METHODS: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. RESULT & CONCLUSION: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.